Overview

Dataset statistics

Number of variables37
Number of observations113387
Missing cells629002
Missing cells (%)15.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory119.7 MiB
Average record size in memory1.1 KiB

Variable types

Numeric14
Categorical18
Boolean2
Unsupported3

Alerts

id has a high cardinality: 113387 distinct values High cardinality
n1 is highly correlated with n2 and 3 other fieldsHigh correlation
n2 is highly correlated with n1 and 3 other fieldsHigh correlation
n3 is highly correlated with n7High correlation
n7 is highly correlated with n1 and 4 other fieldsHigh correlation
n8 is highly correlated with n1 and 2 other fieldsHigh correlation
n10 is highly correlated with n1 and 2 other fieldsHigh correlation
n1 is highly correlated with n2 and 2 other fieldsHigh correlation
n2 is highly correlated with n1 and 2 other fieldsHigh correlation
n3 is highly correlated with n7 and 1 other fieldsHigh correlation
n7 is highly correlated with n1 and 3 other fieldsHigh correlation
n8 is highly correlated with n1 and 1 other fieldsHigh correlation
n10 is highly correlated with n3 and 1 other fieldsHigh correlation
n1 is highly correlated with n2 and 1 other fieldsHigh correlation
n2 is highly correlated with n1 and 1 other fieldsHigh correlation
n7 is highly correlated with n10High correlation
n8 is highly correlated with n1 and 1 other fieldsHigh correlation
n10 is highly correlated with n7High correlation
s17 is highly correlated with s70High correlation
s69 is highly correlated with s16High correlation
s71 is highly correlated with s18High correlation
s18 is highly correlated with s71High correlation
s16 is highly correlated with s69High correlation
s70 is highly correlated with s17High correlation
gender is highly correlated with s16 and 1 other fieldsHigh correlation
s11 is highly correlated with s16 and 3 other fieldsHigh correlation
s16 is highly correlated with gender and 9 other fieldsHigh correlation
s17 is highly correlated with s16 and 4 other fieldsHigh correlation
s18 is highly correlated with s11 and 7 other fieldsHigh correlation
s48 is highly correlated with s16 and 2 other fieldsHigh correlation
s52 is highly correlated with s16 and 5 other fieldsHigh correlation
s69 is highly correlated with gender and 9 other fieldsHigh correlation
s70 is highly correlated with s16 and 4 other fieldsHigh correlation
s71 is highly correlated with s11 and 7 other fieldsHigh correlation
n1 is highly correlated with n2 and 6 other fieldsHigh correlation
n2 is highly correlated with n1 and 6 other fieldsHigh correlation
n3 is highly correlated with n1 and 6 other fieldsHigh correlation
n5 is highly correlated with n6High correlation
n6 is highly correlated with n1 and 6 other fieldsHigh correlation
n7 is highly correlated with n1 and 6 other fieldsHigh correlation
n8 is highly correlated with n1 and 5 other fieldsHigh correlation
n9 is highly correlated with n1 and 4 other fieldsHigh correlation
n10 is highly correlated with n1 and 6 other fieldsHigh correlation
label is highly correlated with s16 and 4 other fieldsHigh correlation
s54 has 103016 (90.9%) missing values Missing
s55 has 100760 (88.9%) missing values Missing
s56 has 113387 (100.0%) missing values Missing
s57 has 113387 (100.0%) missing values Missing
s59 has 113387 (100.0%) missing values Missing
label has 85065 (75.0%) missing values Missing
id is uniformly distributed Uniform
id has unique values Unique
s56 is an unsupported type, check if it needs cleaning or further analysis Unsupported
s57 is an unsupported type, check if it needs cleaning or further analysis Unsupported
s59 is an unsupported type, check if it needs cleaning or further analysis Unsupported
n15 has 16263 (14.3%) zeros Zeros

Reproduction

Analysis started2022-06-10 11:26:52.271716
Analysis finished2022-06-10 11:27:36.520664
Duration44.25 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

Distinct85065
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35445.3179
Minimum0
Maximum85064
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size886.0 KiB
2022-06-10T17:27:36.600665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2834.3
Q114173
median28371
Q356717.5
95-th percentile79394.7
Maximum85064
Range85064
Interquartile range (IQR)42544.5

Descriptive statistics

Standard deviation24898.30891
Coefficient of variation (CV)0.7024428158
Kurtosis-1.105929597
Mean35445.3179
Median Absolute Deviation (MAD)18914
Skewness0.4152946951
Sum4019038261
Variance619925786.8
MonotonicityNot monotonic
2022-06-10T17:27:36.692269image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02
 
< 0.1%
188772
 
< 0.1%
188882
 
< 0.1%
188872
 
< 0.1%
188862
 
< 0.1%
188852
 
< 0.1%
188842
 
< 0.1%
188832
 
< 0.1%
188822
 
< 0.1%
188812
 
< 0.1%
Other values (85055)113367
> 99.9%
ValueCountFrequency (%)
02
< 0.1%
12
< 0.1%
22
< 0.1%
32
< 0.1%
42
< 0.1%
52
< 0.1%
62
< 0.1%
72
< 0.1%
82
< 0.1%
92
< 0.1%
ValueCountFrequency (%)
850641
< 0.1%
850631
< 0.1%
850621
< 0.1%
850611
< 0.1%
850601
< 0.1%
850591
< 0.1%
850581
< 0.1%
850571
< 0.1%
850561
< 0.1%
850551
< 0.1%

id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct113387
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size17.3 MiB
b'gAAAAABinOicS09vrmgh0_JyEHihI13ptO0rCyHP7l76be71PWA2ReUc4HUQn16Fya1z8_VStNnFGaXJF262CgsuMPzOaknSeg=='
 
1
b'gAAAAABinOijkypA566Y7gcGOZ8OlOYWEgg8Mk_5_Pv2WQv910IJOs1JnJKSoswXCF_fc9hm08yby8Qgy2LLY3GzTeVyFWwWGg=='
 
1
b'gAAAAABinOjCKZRJoKGD1wWK4T3XWP5epAiBAcVIbydTFPjPMA2Y59Di1ZpppguOWw49g38Cv7uvTgnOObGkjprb8hQrDYafFQ=='
 
1
b'gAAAAABinOizRXRehUuVFKKTiuJNGRL2FMAjJKnZDN9qvqdHJAFpf4XAWpgxpiRN_pEylabvTRvVHO_iyYSCPrYdr3MbwkTZdA=='
 
1
b'gAAAAABinOimpfPRyZXf4DPuYMn8KlNrfqV6_vIjEtuPfglavGrsL5xyUhtHTtfRRoZfAmwlLKvrz_kMBHTIcKWL7qKmUQPiSg=='
 
1
Other values (113382)
113382 

Length

Max length103
Median length103
Mean length103
Min length103

Characters and Unicode

Total characters11678861
Distinct characters66
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique113387 ?
Unique (%)100.0%

Sample

1st rowb'gAAAAABinOicS09vrmgh0_JyEHihI13ptO0rCyHP7l76be71PWA2ReUc4HUQn16Fya1z8_VStNnFGaXJF262CgsuMPzOaknSeg=='
2nd rowb'gAAAAABinOiWGC1WhR6WYP0DA5ssGv9rIekrWUwCdJ8FvkVcSUl2AquMfWqtOqs3AQYGxS13wQv9Tx4GEkPEl5RnbchazqsZcw=='
3rd rowb'gAAAAABinOibTcOBFIVeA4nVF3FuFz_QX3ZlPPFc21gS9EYdw6Wo8Y5agbzfD6hhsaXZCBdrUQVPpZBXYsODc2PDjER2DX5QcA=='
4th rowb'gAAAAABinOig-g3-Q1ggjlMhfUSdn21Aj5yVVeVvXbisuGUmadvbBh5W28jivd2vgGUWVfHtMdC6vNLrDyFM5NgzILAOorgWGA=='
5th rowb'gAAAAABinOiXdoaNUzihOSbyY1tjWtd5EgMaXkkvH6SVbyppsCh4sW4X5QGqFrLNAcfMQ4NPHOLqbNUVKU-5xxvWCwb5tT91Pw=='

Common Values

ValueCountFrequency (%)
b'gAAAAABinOicS09vrmgh0_JyEHihI13ptO0rCyHP7l76be71PWA2ReUc4HUQn16Fya1z8_VStNnFGaXJF262CgsuMPzOaknSeg=='1
 
< 0.1%
b'gAAAAABinOijkypA566Y7gcGOZ8OlOYWEgg8Mk_5_Pv2WQv910IJOs1JnJKSoswXCF_fc9hm08yby8Qgy2LLY3GzTeVyFWwWGg=='1
 
< 0.1%
b'gAAAAABinOjCKZRJoKGD1wWK4T3XWP5epAiBAcVIbydTFPjPMA2Y59Di1ZpppguOWw49g38Cv7uvTgnOObGkjprb8hQrDYafFQ=='1
 
< 0.1%
b'gAAAAABinOizRXRehUuVFKKTiuJNGRL2FMAjJKnZDN9qvqdHJAFpf4XAWpgxpiRN_pEylabvTRvVHO_iyYSCPrYdr3MbwkTZdA=='1
 
< 0.1%
b'gAAAAABinOimpfPRyZXf4DPuYMn8KlNrfqV6_vIjEtuPfglavGrsL5xyUhtHTtfRRoZfAmwlLKvrz_kMBHTIcKWL7qKmUQPiSg=='1
 
< 0.1%
b'gAAAAABinOiohulV4_PQMc9bC3YjojrSwIT0ZG_HeipXeFsBI9Y9RHlCXEOzU877fvlmgQ_dwLza2DRT_jXOMPCMjzdry2MJYg=='1
 
< 0.1%
b'gAAAAABinOjI1UGboxX48KQbd2u9ysIRgrwMrpqRujPqqSJrBk0DQKIDR8t3Q6eH79QAzghbnfENqXyOyTHjUUw7n2EUHbiPaA=='1
 
< 0.1%
b'gAAAAABinOju_-xa8S639RpujV1PC6zGcecXfawVw5wk466gJEM0KR8Oe66kZUgI587W_Jy2XSOEApDkMv2PmuUAmSCtu5ZjXg=='1
 
< 0.1%
b'gAAAAABinOjN7wpl3CMaefP78peoKp5MMl7iq-2prQNjjMQZirsLg6MT6rDPGqM4HHw_5-BHYLsgiBxo0NVjF9J-ohp7W8A1kg=='1
 
< 0.1%
b'gAAAAABinOjfXFgXzqMW8g9b2CDQ0sGYD5BmhLCey62hZ_mZjLFDuYhZz-tm5EqVDtYsPeGjNFrw54avw2kOpCgi3o2QROXhWQ=='1
 
< 0.1%
Other values (113377)113377
> 99.9%

Length

2022-06-10T17:27:36.779271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
b'gaaaaabinoics09vrmgh0_jyehihi13pto0rcyhp7l76be71pwa2reuc4huqn16fya1z8_vstnnfgaxjf262cgsumpzoaknseg1
 
< 0.1%
b'gaaaaabinoizfrale3zmaqhl1ux4ffui5lz9r-gkrk06cjdh_rkebne5j6ayf0un3gdpfgshwrn1xaiiniupizjnmkloolieaa1
 
< 0.1%
b'gaaaaabinoiyfrgmhzu5slaayktwjbrhpnjcypvxqpnfcegg_-eygdg_9f43uyqqd9ok9mkzlmuyqmf9ly0pk0gozfdqzbfmia1
 
< 0.1%
b'gaaaaabinoixqjg5rjpzshsaqrjhp8u_21fwetw89efnxr9ftb8fh3plyl5ev8psgkzrz0gybpckfs3ldv44cbp12ftatgrtfw1
 
< 0.1%
b'gaaaaabinoic9rpk09lv7q2k7bmbzxst0zlua8sft7x0zu92omuyqexuohsyx-ubx1jlsbooyn9pptykg6nas-hibfvbnv3wow1
 
< 0.1%
b'gaaaaabinoiwr-5mklxyj9hkimylh2xnhjeywiipt94iypkqenbqh9nezdgg27apxcq4nzhzsc8-sqrrxsggxemge3oslznzfw1
 
< 0.1%
b'gaaaaabinoiwojfncdi_4ynxirygki_rpyp_4lrn0qudm6b6ug2xeumlslet6b3jsfms1yp3wschxf0lqlgpla_mdzsh2xgiuq1
 
< 0.1%
b'gaaaaabinoieinoucnx-mhyzbo0_dldtmh1cb7opn_mncdiqiryjivxswhwugykxfd87triclgcsjjtzemml9anbpsyt9iusza1
 
< 0.1%
b'gaaaaabinoid6obuufesrjiycahsi4zryczn53dwd9qqdri7wrujd5cxyhipvrzgbddy1fmivzvbtn4n4pbgojej2abjimfpqq1
 
< 0.1%
b'gaaaaabinoizn9dh3ix0mez68xy7n84c4nbp_8trnlygwpztchz4idwdiwt9i3jrefbwmiq7biugodmgv2ludfkllvjrxq8qyq1
 
< 0.1%
Other values (113377)113377
> 99.9%

Most occurring characters

ValueCountFrequency (%)
A747195
 
6.4%
i348181
 
3.0%
g295632
 
2.5%
b267627
 
2.3%
n265934
 
2.3%
O264428
 
2.3%
B264245
 
2.3%
'226774
 
1.9%
=226774
 
1.9%
j183357
 
1.6%
Other values (56)8588714
73.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter4790875
41.0%
Lowercase Letter4604551
39.4%
Decimal Number1526176
 
13.1%
Other Punctuation226774
 
1.9%
Math Symbol226774
 
1.9%
Dash Punctuation152075
 
1.3%
Connector Punctuation151636
 
1.3%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A747195
 
15.6%
O264428
 
5.5%
B264245
 
5.5%
Q179762
 
3.8%
Z154206
 
3.2%
W154160
 
3.2%
X153768
 
3.2%
Y153476
 
3.2%
V152530
 
3.2%
P151819
 
3.2%
Other values (16)2415286
50.4%
Lowercase Letter
ValueCountFrequency (%)
i348181
 
7.6%
g295632
 
6.4%
b267627
 
5.8%
n265934
 
5.8%
j183357
 
4.0%
w181612
 
3.9%
c154269
 
3.4%
d153971
 
3.3%
k153953
 
3.3%
e153769
 
3.3%
Other values (16)2446246
53.1%
Decimal Number
ValueCountFrequency (%)
6153165
10.0%
2153116
10.0%
1153057
10.0%
0152879
10.0%
4152655
10.0%
3152615
10.0%
5152496
10.0%
7152419
10.0%
9152178
10.0%
8151596
9.9%
Other Punctuation
ValueCountFrequency (%)
'226774
100.0%
Math Symbol
ValueCountFrequency (%)
=226774
100.0%
Dash Punctuation
ValueCountFrequency (%)
-152075
100.0%
Connector Punctuation
ValueCountFrequency (%)
_151636
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin9395426
80.4%
Common2283435
 
19.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A747195
 
8.0%
i348181
 
3.7%
g295632
 
3.1%
b267627
 
2.8%
n265934
 
2.8%
O264428
 
2.8%
B264245
 
2.8%
j183357
 
2.0%
w181612
 
1.9%
Q179762
 
1.9%
Other values (42)6397453
68.1%
Common
ValueCountFrequency (%)
'226774
 
9.9%
=226774
 
9.9%
6153165
 
6.7%
2153116
 
6.7%
1153057
 
6.7%
0152879
 
6.7%
4152655
 
6.7%
3152615
 
6.7%
5152496
 
6.7%
7152419
 
6.7%
Other values (4)607485
26.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII11678861
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A747195
 
6.4%
i348181
 
3.0%
g295632
 
2.5%
b267627
 
2.3%
n265934
 
2.3%
O264428
 
2.3%
B264245
 
2.3%
'226774
 
1.9%
=226774
 
1.9%
j183357
 
1.6%
Other values (56)8588714
73.5%

gender
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
M
90851 
F
22536 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113387
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowF
5th rowM

Common Values

ValueCountFrequency (%)
M90851
80.1%
F22536
 
19.9%

Length

2022-06-10T17:27:36.850272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T17:27:36.930272image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
m90851
80.1%
f22536
 
19.9%

Most occurring characters

ValueCountFrequency (%)
M90851
80.1%
F22536
 
19.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter113387
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M90851
80.1%
F22536
 
19.9%

Most occurring scripts

ValueCountFrequency (%)
Latin113387
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M90851
80.1%
F22536
 
19.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII113387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M90851
80.1%
F22536
 
19.9%

s11
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size110.9 KiB
True
92728 
False
20659 
ValueCountFrequency (%)
True92728
81.8%
False20659
 
18.2%
2022-06-10T17:27:36.994271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

s12
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size110.9 KiB
True
98886 
False
14501 
ValueCountFrequency (%)
True98886
87.2%
False14501
 
12.8%
2022-06-10T17:27:37.055852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

s13
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
1
108963 
0
 
4424

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113387
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1108963
96.1%
04424
 
3.9%

Length

2022-06-10T17:27:37.115857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T17:27:37.181859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1108963
96.1%
04424
 
3.9%

Most occurring characters

ValueCountFrequency (%)
1108963
96.1%
04424
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number113387
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1108963
96.1%
04424
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
Common113387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1108963
96.1%
04424
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII113387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1108963
96.1%
04424
 
3.9%

s16
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
D
65844 
B
44399 
C
 
2023
A
 
1121

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113387
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowD
4th rowD
5th rowB

Common Values

ValueCountFrequency (%)
D65844
58.1%
B44399
39.2%
C2023
 
1.8%
A1121
 
1.0%

Length

2022-06-10T17:27:37.239859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T17:27:37.310857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
d65844
58.1%
b44399
39.2%
c2023
 
1.8%
a1121
 
1.0%

Most occurring characters

ValueCountFrequency (%)
D65844
58.1%
B44399
39.2%
C2023
 
1.8%
A1121
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter113387
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D65844
58.1%
B44399
39.2%
C2023
 
1.8%
A1121
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Latin113387
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D65844
58.1%
B44399
39.2%
C2023
 
1.8%
A1121
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII113387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D65844
58.1%
B44399
39.2%
C2023
 
1.8%
A1121
 
1.0%

s17
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
D
88077 
C
13106 
B
11704 
A
 
500

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113387
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowD
2nd rowD
3rd rowD
4th rowD
5th rowD

Common Values

ValueCountFrequency (%)
D88077
77.7%
C13106
 
11.6%
B11704
 
10.3%
A500
 
0.4%

Length

2022-06-10T17:27:37.374464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T17:27:37.446469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
d88077
77.7%
c13106
 
11.6%
b11704
 
10.3%
a500
 
0.4%

Most occurring characters

ValueCountFrequency (%)
D88077
77.7%
C13106
 
11.6%
B11704
 
10.3%
A500
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter113387
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D88077
77.7%
C13106
 
11.6%
B11704
 
10.3%
A500
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Latin113387
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
D88077
77.7%
C13106
 
11.6%
B11704
 
10.3%
A500
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII113387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
D88077
77.7%
C13106
 
11.6%
B11704
 
10.3%
A500
 
0.4%

s18
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
B
88821 
D
12699 
C
11589 
A
 
278

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113387
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowD

Common Values

ValueCountFrequency (%)
B88821
78.3%
D12699
 
11.2%
C11589
 
10.2%
A278
 
0.2%

Length

2022-06-10T17:27:37.511466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T17:27:37.582466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
b88821
78.3%
d12699
 
11.2%
c11589
 
10.2%
a278
 
0.2%

Most occurring characters

ValueCountFrequency (%)
B88821
78.3%
D12699
 
11.2%
C11589
 
10.2%
A278
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter113387
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B88821
78.3%
D12699
 
11.2%
C11589
 
10.2%
A278
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin113387
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B88821
78.3%
D12699
 
11.2%
C11589
 
10.2%
A278
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII113387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B88821
78.3%
D12699
 
11.2%
C11589
 
10.2%
A278
 
0.2%

s48
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
1
60808 
0
52579 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113387
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
160808
53.6%
052579
46.4%

Length

2022-06-10T17:27:37.647464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T17:27:37.715464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
160808
53.6%
052579
46.4%

Most occurring characters

ValueCountFrequency (%)
160808
53.6%
052579
46.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number113387
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
160808
53.6%
052579
46.4%

Most occurring scripts

ValueCountFrequency (%)
Common113387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
160808
53.6%
052579
46.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII113387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
160808
53.6%
052579
46.4%

s52
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
1
66093 
l
42564 
0
 
3905
o
 
825

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113387
Distinct characters4
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th rowl

Common Values

ValueCountFrequency (%)
166093
58.3%
l42564
37.5%
03905
 
3.4%
o825
 
0.7%

Length

2022-06-10T17:27:37.774466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T17:27:37.845466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
166093
58.3%
l42564
37.5%
03905
 
3.4%
o825
 
0.7%

Most occurring characters

ValueCountFrequency (%)
166093
58.3%
l42564
37.5%
03905
 
3.4%
o825
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number69998
61.7%
Lowercase Letter43389
38.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
166093
94.4%
03905
 
5.6%
Lowercase Letter
ValueCountFrequency (%)
l42564
98.1%
o825
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common69998
61.7%
Latin43389
38.3%

Most frequent character per script

Common
ValueCountFrequency (%)
166093
94.4%
03905
 
5.6%
Latin
ValueCountFrequency (%)
l42564
98.1%
o825
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII113387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
166093
58.3%
l42564
37.5%
03905
 
3.4%
o825
 
0.7%

s53
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.4 MiB
103232 
 
10155

Length

Max length2
Median length2
Mean length1.910439468
Min length1

Characters and Unicode

Total characters216619
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
103232
91.0%
10155
 
9.0%

Length

2022-06-10T17:27:38.037466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T17:27:38.106466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

Most occurring characters

ValueCountFrequency (%)
216619
100.0%

Most occurring categories

ValueCountFrequency (%)
Space Separator216619
100.0%

Most frequent character per category

Space Separator
ValueCountFrequency (%)
216619
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common216619
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
216619
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII216619
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
216619
100.0%

s54
Categorical

MISSING

Distinct9
Distinct (%)0.1%
Missing103016
Missing (%)90.9%
Memory size3.7 MiB
22
1216 
ba
1201 
b2
1175 
bb
1151 
ab
1150 
Other values (4)
4478 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters20742
Distinct characters3
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowb2
2nd rowaa
3rd rowb2
4th row22
5th row2a

Common Values

ValueCountFrequency (%)
221216
 
1.1%
ba1201
 
1.1%
b21175
 
1.0%
bb1151
 
1.0%
ab1150
 
1.0%
a21148
 
1.0%
2b1143
 
1.0%
2a1120
 
1.0%
aa1067
 
0.9%
(Missing)103016
90.9%

Length

2022-06-10T17:27:38.164464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T17:27:38.249466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
221216
11.7%
ba1201
11.6%
b21175
11.3%
bb1151
11.1%
ab1150
11.1%
a21148
11.1%
2b1143
11.0%
2a1120
10.8%
aa1067
10.3%

Most occurring characters

ValueCountFrequency (%)
27018
33.8%
b6971
33.6%
a6753
32.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13724
66.2%
Decimal Number7018
33.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b6971
50.8%
a6753
49.2%
Decimal Number
ValueCountFrequency (%)
27018
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin13724
66.2%
Common7018
33.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
b6971
50.8%
a6753
49.2%
Common
ValueCountFrequency (%)
27018
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII20742
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
27018
33.8%
b6971
33.6%
a6753
32.6%

s55
Categorical

MISSING

Distinct9
Distinct (%)0.1%
Missing100760
Missing (%)88.9%
Memory size3.8 MiB
2K
1473 
k2
1464 
Kk
1421 
2k
1419 
K2
1398 
Other values (4)
5452 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters25254
Distinct characters3
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowkK
2nd rowkk
3rd row2K
4th rowk2
5th rowKK

Common Values

ValueCountFrequency (%)
2K1473
 
1.3%
k21464
 
1.3%
Kk1421
 
1.3%
2k1419
 
1.3%
K21398
 
1.2%
221386
 
1.2%
kk1380
 
1.2%
KK1378
 
1.2%
kK1308
 
1.2%
(Missing)100760
88.9%

Length

2022-06-10T17:27:38.339466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T17:27:38.426464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
kk5487
43.5%
2k2892
22.9%
k22862
22.7%
221386
 
11.0%

Most occurring characters

ValueCountFrequency (%)
28526
33.8%
k8372
33.2%
K8356
33.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8526
33.8%
Lowercase Letter8372
33.2%
Uppercase Letter8356
33.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
28526
100.0%
Lowercase Letter
ValueCountFrequency (%)
k8372
100.0%
Uppercase Letter
ValueCountFrequency (%)
K8356
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16728
66.2%
Common8526
33.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
k8372
50.0%
K8356
50.0%
Common
ValueCountFrequency (%)
28526
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII25254
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
28526
33.8%
k8372
33.2%
K8356
33.1%

s56
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing113387
Missing (%)100.0%
Memory size886.0 KiB

s57
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing113387
Missing (%)100.0%
Memory size886.0 KiB

s58
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
B
103224 
A
 
10163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113387
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowB
3rd rowB
4th rowB
5th rowB

Common Values

ValueCountFrequency (%)
B103224
91.0%
A10163
 
9.0%

Length

2022-06-10T17:27:38.515469image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T17:27:38.583464image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
b103224
91.0%
a10163
 
9.0%

Most occurring characters

ValueCountFrequency (%)
B103224
91.0%
A10163
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter113387
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
B103224
91.0%
A10163
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Latin113387
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
B103224
91.0%
A10163
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII113387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
B103224
91.0%
A10163
 
9.0%

s59
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing113387
Missing (%)100.0%
Memory size886.0 KiB

s69
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
x
65844 
~1
44399 
C`
 
2023
0
 
1121

Length

Max length2
Median length1
Mean length1.409412014
Min length1

Characters and Unicode

Total characters159809
Distinct characters6
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowx
2nd rowx
3rd rowx
4th rowx
5th row~1

Common Values

ValueCountFrequency (%)
x65844
58.1%
~144399
39.2%
C`2023
 
1.8%
01121
 
1.0%

Length

2022-06-10T17:27:38.645468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T17:27:38.722466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
x65844
58.1%
144399
39.2%
c2023
 
1.8%
01121
 
1.0%

Most occurring characters

ValueCountFrequency (%)
x65844
41.2%
~44399
27.8%
144399
27.8%
C2023
 
1.3%
`2023
 
1.3%
01121
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter65844
41.2%
Decimal Number45520
28.5%
Math Symbol44399
27.8%
Uppercase Letter2023
 
1.3%
Modifier Symbol2023
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
144399
97.5%
01121
 
2.5%
Lowercase Letter
ValueCountFrequency (%)
x65844
100.0%
Math Symbol
ValueCountFrequency (%)
~44399
100.0%
Uppercase Letter
ValueCountFrequency (%)
C2023
100.0%
Modifier Symbol
ValueCountFrequency (%)
`2023
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common91942
57.5%
Latin67867
42.5%

Most frequent character per script

Common
ValueCountFrequency (%)
~44399
48.3%
144399
48.3%
`2023
 
2.2%
01121
 
1.2%
Latin
ValueCountFrequency (%)
x65844
97.0%
C2023
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII159809
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
x65844
41.2%
~44399
27.8%
144399
27.8%
C2023
 
1.3%
`2023
 
1.3%
01121
 
0.7%

s70
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.7 MiB
op: D
88077 
op: C
13106 
op: B
11704 
op: A
 
500

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters566935
Distinct characters8
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowop: D
2nd rowop: D
3rd rowop: D
4th rowop: D
5th rowop: D

Common Values

ValueCountFrequency (%)
op: D88077
77.7%
op: C13106
 
11.6%
op: B11704
 
10.3%
op: A500
 
0.4%

Length

2022-06-10T17:27:38.785465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T17:27:38.858466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
op113387
50.0%
d88077
38.8%
c13106
 
5.8%
b11704
 
5.2%
a500
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o113387
20.0%
p113387
20.0%
:113387
20.0%
113387
20.0%
D88077
15.5%
C13106
 
2.3%
B11704
 
2.1%
A500
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter226774
40.0%
Other Punctuation113387
20.0%
Space Separator113387
20.0%
Uppercase Letter113387
20.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
D88077
77.7%
C13106
 
11.6%
B11704
 
10.3%
A500
 
0.4%
Lowercase Letter
ValueCountFrequency (%)
o113387
50.0%
p113387
50.0%
Other Punctuation
ValueCountFrequency (%)
:113387
100.0%
Space Separator
ValueCountFrequency (%)
113387
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin340161
60.0%
Common226774
40.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
o113387
33.3%
p113387
33.3%
D88077
25.9%
C13106
 
3.9%
B11704
 
3.4%
A500
 
0.1%
Common
ValueCountFrequency (%)
:113387
50.0%
113387
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII566935
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o113387
20.0%
p113387
20.0%
:113387
20.0%
113387
20.0%
D88077
15.5%
C13106
 
2.3%
B11704
 
2.1%
A500
 
0.1%

s71
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
b
88821 
d
12699 
c
11589 
a
 
278

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113387
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowb
2nd rowb
3rd rowb
4th rowb
5th rowd

Common Values

ValueCountFrequency (%)
b88821
78.3%
d12699
 
11.2%
c11589
 
10.2%
a278
 
0.2%

Length

2022-06-10T17:27:38.924465image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T17:27:38.997468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
b88821
78.3%
d12699
 
11.2%
c11589
 
10.2%
a278
 
0.2%

Most occurring characters

ValueCountFrequency (%)
b88821
78.3%
d12699
 
11.2%
c11589
 
10.2%
a278
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter113387
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
b88821
78.3%
d12699
 
11.2%
c11589
 
10.2%
a278
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin113387
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
b88821
78.3%
d12699
 
11.2%
c11589
 
10.2%
a278
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII113387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
b88821
78.3%
d12699
 
11.2%
c11589
 
10.2%
a278
 
0.2%

n1
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct113363
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.72889787
Minimum1.774084461
Maximum21.11485611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size886.0 KiB
2022-06-10T17:27:39.077466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.774084461
5-th percentile4.124044859
Q16.787560437
median10.06541224
Q314.81984078
95-th percentile18.05616167
Maximum21.11485611
Range19.34077165
Interquartile range (IQR)8.032280343

Descriptive statistics

Standard deviation4.601925623
Coefficient of variation (CV)0.4289280855
Kurtosis-1.107815423
Mean10.72889787
Median Absolute Deviation (MAD)3.727073984
Skewness0.2215451042
Sum1216517.543
Variance21.17771944
MonotonicityNot monotonic
2022-06-10T17:27:39.170468image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.0938141032
 
< 0.1%
5.6993849252
 
< 0.1%
15.919071442
 
< 0.1%
12.669613812
 
< 0.1%
2.9436016952
 
< 0.1%
7.3097789072
 
< 0.1%
6.9131609852
 
< 0.1%
15.973428612
 
< 0.1%
15.402591472
 
< 0.1%
16.04562062
 
< 0.1%
Other values (113353)113367
> 99.9%
ValueCountFrequency (%)
1.7740844611
< 0.1%
1.9390020531
< 0.1%
1.9580421641
< 0.1%
2.0241128651
< 0.1%
2.0802104551
< 0.1%
2.0851715361
< 0.1%
2.0891454541
< 0.1%
2.1319337031
< 0.1%
2.160785241
< 0.1%
2.1644835771
< 0.1%
ValueCountFrequency (%)
21.114856111
< 0.1%
21.057109781
< 0.1%
21.025354651
< 0.1%
21.018464331
< 0.1%
21.01010791
< 0.1%
21.007412581
< 0.1%
20.988513511
< 0.1%
20.951008411
< 0.1%
20.886913171
< 0.1%
20.882732831
< 0.1%

n2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct113366
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.667674863
Minimum0.296709497
Maximum3.168021053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size886.0 KiB
2022-06-10T17:27:39.271466image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.296709497
5-th percentile0.7179505193
Q11.049385924
median1.516055163
Q32.263937922
95-th percentile2.877659488
Maximum3.168021053
Range2.871311556
Interquartile range (IQR)1.214551998

Descriptive statistics

Standard deviation0.6934665555
Coefficient of variation (CV)0.4158283913
Kurtosis-1.1431
Mean1.667674863
Median Absolute Deviation (MAD)0.586635793
Skewness0.2616204153
Sum189092.6497
Variance0.4808958636
MonotonicityNot monotonic
2022-06-10T17:27:39.361102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.8218569272
 
< 0.1%
1.1973139772
 
< 0.1%
1.0493627642
 
< 0.1%
0.51775372
 
< 0.1%
2.5433410572
 
< 0.1%
1.1081385322
 
< 0.1%
1.132792022
 
< 0.1%
2.8227036172
 
< 0.1%
2.4287190762
 
< 0.1%
0.927583572
 
< 0.1%
Other values (113356)113367
> 99.9%
ValueCountFrequency (%)
0.2967094971
< 0.1%
0.3117266561
< 0.1%
0.3272813961
< 0.1%
0.3303695341
< 0.1%
0.336190841
< 0.1%
0.3385125381
< 0.1%
0.3388180451
< 0.1%
0.3477959881
< 0.1%
0.3497725191
< 0.1%
0.3525445831
< 0.1%
ValueCountFrequency (%)
3.1680210531
< 0.1%
3.1640883231
< 0.1%
3.1625760721
< 0.1%
3.1580334471
< 0.1%
3.1572575331
< 0.1%
3.1514860941
< 0.1%
3.1486160261
< 0.1%
3.1468934431
< 0.1%
3.1467525031
< 0.1%
3.1460128081
< 0.1%

n3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.593560108
Minimum0
Maximum10
Zeros558
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size886.0 KiB
2022-06-10T17:27:39.443104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median4
Q36
95-th percentile8
Maximum10
Range10
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.214352861
Coefficient of variation (CV)0.4820559238
Kurtosis-1.151281205
Mean4.593560108
Median Absolute Deviation (MAD)2
Skewness0.2862941049
Sum520850
Variance4.903358594
MonotonicityNot monotonic
2022-06-10T17:27:39.511102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
340305
35.5%
624530
21.6%
711847
 
10.4%
211708
 
10.3%
89795
 
8.6%
44943
 
4.4%
14064
 
3.6%
94006
 
3.5%
51630
 
1.4%
0558
 
0.5%
ValueCountFrequency (%)
0558
 
0.5%
14064
 
3.6%
211708
 
10.3%
340305
35.5%
44943
 
4.4%
51630
 
1.4%
624530
21.6%
711847
 
10.4%
89795
 
8.6%
94006
 
3.5%
ValueCountFrequency (%)
101
 
< 0.1%
94006
 
3.5%
89795
 
8.6%
711847
 
10.4%
624530
21.6%
51630
 
1.4%
44943
 
4.4%
340305
35.5%
211708
 
10.3%
14064
 
3.6%

n4
Real number (ℝ≥0)

Distinct113365
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.976887119
Minimum1.700369869
Maximum8.613252434
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size886.0 KiB
2022-06-10T17:27:39.596104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.700369869
5-th percentile2.160462834
Q12.619604941
median4.678687354
Q37.202718296
95-th percentile8.138034148
Maximum8.613252434
Range6.912882565
Interquartile range (IQR)4.583113355

Descriptive statistics

Standard deviation2.276387425
Coefficient of variation (CV)0.4573918136
Kurtosis-1.649140808
Mean4.976887119
Median Absolute Deviation (MAD)2.209445207
Skewness0.1040968649
Sum564314.2998
Variance5.18193971
MonotonicityNot monotonic
2022-06-10T17:27:39.690104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.8652905262
 
< 0.1%
7.2555001142
 
< 0.1%
2.1896355762
 
< 0.1%
7.9816557182
 
< 0.1%
3.4277013442
 
< 0.1%
2.8148422792
 
< 0.1%
3.9040740552
 
< 0.1%
2.2919129972
 
< 0.1%
7.8361245852
 
< 0.1%
7.9363596042
 
< 0.1%
Other values (113355)113367
> 99.9%
ValueCountFrequency (%)
1.7003698691
< 0.1%
1.7195169661
< 0.1%
1.7347378441
< 0.1%
1.7535484731
< 0.1%
1.7678639421
< 0.1%
1.7702156351
< 0.1%
1.7737646461
< 0.1%
1.7750477741
< 0.1%
1.7767126371
< 0.1%
1.7794367351
< 0.1%
ValueCountFrequency (%)
8.6132524341
< 0.1%
8.5946203311
< 0.1%
8.5944042131
< 0.1%
8.574914361
< 0.1%
8.5718020721
< 0.1%
8.5651659351
< 0.1%
8.5579996781
< 0.1%
8.5391052141
< 0.1%
8.5352010871
< 0.1%
8.5341779891
< 0.1%

n5
Real number (ℝ)

HIGH CORRELATION

Distinct112707
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-32.60482695
Minimum-33.16758925
Maximum-32.28077893
Zeros0
Zeros (%)0.0%
Negative113387
Negative (%)100.0%
Memory size886.0 KiB
2022-06-10T17:27:39.792104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-33.16758925
5-th percentile-33.03599128
Q1-32.77962034
median-32.57518866
Q3-32.39457643
95-th percentile-32.32864307
Maximum-32.28077893
Range0.88681032
Interquartile range (IQR)0.385043915

Descriptive statistics

Standard deviation0.2384319083
Coefficient of variation (CV)-0.007312779445
Kurtosis-1.006521473
Mean-32.60482695
Median Absolute Deviation (MAD)0.1967408
Skewness-0.5624198378
Sum-3696963.513
Variance0.0568497749
MonotonicityNot monotonic
2022-06-10T17:27:39.886101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-32.575991753
 
< 0.1%
-32.575987923
 
< 0.1%
-32.396064223
 
< 0.1%
-32.452216193
 
< 0.1%
-32.577065193
 
< 0.1%
-32.577299773
 
< 0.1%
-32.576264443
 
< 0.1%
-32.575280453
 
< 0.1%
-32.395681453
 
< 0.1%
-32.510108032
 
< 0.1%
Other values (112697)113358
> 99.9%
ValueCountFrequency (%)
-33.167589251
< 0.1%
-33.160250221
< 0.1%
-33.150068951
< 0.1%
-33.149008281
< 0.1%
-33.147914331
< 0.1%
-33.147746731
< 0.1%
-33.147252461
< 0.1%
-33.145396141
< 0.1%
-33.143342631
< 0.1%
-33.143001131
< 0.1%
ValueCountFrequency (%)
-32.280778931
< 0.1%
-32.284389171
< 0.1%
-32.284797011
< 0.1%
-32.285136151
< 0.1%
-32.287751931
< 0.1%
-32.288488321
< 0.1%
-32.289065051
< 0.1%
-32.289180131
< 0.1%
-32.289863521
< 0.1%
-32.289876551
< 0.1%

n6
Real number (ℝ≥0)

HIGH CORRELATION

Distinct113069
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01588148892
Minimum0.000270732
Maximum0.030076548
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size886.0 KiB
2022-06-10T17:27:39.986104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0.000270732
5-th percentile0.0051016514
Q10.010491255
median0.016583825
Q30.0199328425
95-th percentile0.0273264422
Maximum0.030076548
Range0.029805816
Interquartile range (IQR)0.0094415875

Descriptive statistics

Standard deviation0.006760490411
Coefficient of variation (CV)0.4256836651
Kurtosis-0.7167156142
Mean0.01588148892
Median Absolute Deviation (MAD)0.005084856
Skewness0.03457326076
Sum1800.754384
Variance4.57042306 × 10-5
MonotonicityNot monotonic
2022-06-10T17:27:40.080102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0103545993
 
< 0.1%
0.0165694372
 
< 0.1%
0.0189952752
 
< 0.1%
0.0294861962
 
< 0.1%
0.0181451872
 
< 0.1%
0.0185200212
 
< 0.1%
0.0158286822
 
< 0.1%
0.0164597122
 
< 0.1%
0.0067077722
 
< 0.1%
0.0059664072
 
< 0.1%
Other values (113059)113366
> 99.9%
ValueCountFrequency (%)
0.0002707321
< 0.1%
0.0003341561
< 0.1%
0.0005113221
< 0.1%
0.0005173451
< 0.1%
0.0005844331
< 0.1%
0.0005917871
< 0.1%
0.0006657681
< 0.1%
0.000680251
< 0.1%
0.0007184281
< 0.1%
0.0007194151
< 0.1%
ValueCountFrequency (%)
0.0300765481
< 0.1%
0.0300686881
< 0.1%
0.030050241
< 0.1%
0.0300484731
< 0.1%
0.0300469351
< 0.1%
0.0300460351
< 0.1%
0.030038421
< 0.1%
0.0300264511
< 0.1%
0.0300238671
< 0.1%
0.0300232061
< 0.1%

n7
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct113367
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.112285321
Minimum-9.517985619
Maximum-8.581279248
Zeros0
Zeros (%)0.0%
Negative113387
Negative (%)100.0%
Memory size886.0 KiB
2022-06-10T17:27:40.180104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-9.517985619
5-th percentile-9.36790321
Q1-9.254451765
median-9.179367667
Q3-8.984399901
95-th percentile-8.731592407
Maximum-8.581279248
Range0.936706371
Interquartile range (IQR)0.2700518635

Descriptive statistics

Standard deviation0.1972145113
Coefficient of variation (CV)-0.0216427059
Kurtosis-0.53171111
Mean-9.112285321
Median Absolute Deviation (MAD)0.105877586
Skewness0.6972478535
Sum-1033214.696
Variance0.03889356347
MonotonicityNot monotonic
2022-06-10T17:27:40.407104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9.4311264192
 
< 0.1%
-9.1395761742
 
< 0.1%
-9.2603793062
 
< 0.1%
-9.0503090792
 
< 0.1%
-9.26003492
 
< 0.1%
-9.2629810862
 
< 0.1%
-9.0529347552
 
< 0.1%
-9.1446231412
 
< 0.1%
-9.2125981232
 
< 0.1%
-9.2433984882
 
< 0.1%
Other values (113357)113367
> 99.9%
ValueCountFrequency (%)
-9.5179856191
< 0.1%
-9.5169438411
< 0.1%
-9.5121575191
< 0.1%
-9.5086306661
< 0.1%
-9.508032261
< 0.1%
-9.5033923711
< 0.1%
-9.5030405861
< 0.1%
-9.5013666091
< 0.1%
-9.5007895241
< 0.1%
-9.4962526041
< 0.1%
ValueCountFrequency (%)
-8.5812792481
< 0.1%
-8.5856638171
< 0.1%
-8.5865828171
< 0.1%
-8.5893461441
< 0.1%
-8.590060571
< 0.1%
-8.5916000121
< 0.1%
-8.5920634091
< 0.1%
-8.5928640861
< 0.1%
-8.5944044941
< 0.1%
-8.5956958661
< 0.1%

n8
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct113363
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.635853896
Minimum1.16905687
Maximum2.186356921
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size886.0 KiB
2022-06-10T17:27:40.503103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.16905687
5-th percentile1.411766804
Q11.493679085
median1.554885988
Q31.784963247
95-th percentile2.027568444
Maximum2.186356921
Range1.017300051
Interquartile range (IQR)0.291284162

Descriptive statistics

Standard deviation0.1898397667
Coefficient of variation (CV)0.116049341
Kurtosis-0.1886849229
Mean1.635853896
Median Absolute Deviation (MAD)0.091809278
Skewness0.842356928
Sum185484.5657
Variance0.03603913702
MonotonicityNot monotonic
2022-06-10T17:27:40.600104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.5160650722
 
< 0.1%
1.4636227722
 
< 0.1%
1.4761910332
 
< 0.1%
1.517193512
 
< 0.1%
1.4723905552
 
< 0.1%
1.4887927952
 
< 0.1%
1.5553040092
 
< 0.1%
1.5672763912
 
< 0.1%
1.5162766022
 
< 0.1%
1.4753398272
 
< 0.1%
Other values (113353)113367
> 99.9%
ValueCountFrequency (%)
1.169056871
< 0.1%
1.1784693911
< 0.1%
1.1813292661
< 0.1%
1.1866669731
< 0.1%
1.1910157491
< 0.1%
1.1917014411
< 0.1%
1.1943818411
< 0.1%
1.1949439921
< 0.1%
1.1964724851
< 0.1%
1.1969251971
< 0.1%
ValueCountFrequency (%)
2.1863569211
< 0.1%
2.1825252561
< 0.1%
2.1814905661
< 0.1%
2.1778056111
< 0.1%
2.1756068541
< 0.1%
2.1755670321
< 0.1%
2.1755585771
< 0.1%
2.1750666061
< 0.1%
2.1748025021
< 0.1%
2.1742374161
< 0.1%

n9
Real number (ℝ≥0)

HIGH CORRELATION

Distinct113362
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.325540283
Minimum2.410793158
Maximum11.26997233
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size886.0 KiB
2022-06-10T17:27:40.699103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.410793158
5-th percentile2.856418959
Q13.519582709
median4.852390828
Q36.623488795
95-th percentile9.316947379
Maximum11.26997233
Range8.859179172
Interquartile range (IQR)3.103906086

Descriptive statistics

Standard deviation2.165350215
Coefficient of variation (CV)0.4065972841
Kurtosis-0.3605864102
Mean5.325540283
Median Absolute Deviation (MAD)1.469872557
Skewness0.7767584196
Sum603847.0361
Variance4.688741555
MonotonicityNot monotonic
2022-06-10T17:27:40.792104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.1463346012
 
< 0.1%
5.7631498152
 
< 0.1%
4.844212492
 
< 0.1%
3.4148397992
 
< 0.1%
3.5876117612
 
< 0.1%
5.8619670882
 
< 0.1%
5.6263164372
 
< 0.1%
3.1091810812
 
< 0.1%
3.8374388212
 
< 0.1%
6.5457269632
 
< 0.1%
Other values (113352)113367
> 99.9%
ValueCountFrequency (%)
2.4107931581
< 0.1%
2.4742810221
< 0.1%
2.4791535631
< 0.1%
2.4817581281
< 0.1%
2.4840341781
< 0.1%
2.4890792291
< 0.1%
2.4914900661
< 0.1%
2.4935266281
< 0.1%
2.5023951411
< 0.1%
2.5024911351
< 0.1%
ValueCountFrequency (%)
11.269972331
< 0.1%
11.257386651
< 0.1%
11.245954481
< 0.1%
11.21486951
< 0.1%
11.213377441
< 0.1%
11.204844421
< 0.1%
11.193197871
< 0.1%
11.19024541
< 0.1%
11.185244871
< 0.1%
11.182215321
< 0.1%

n10
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct113356
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.039585076
Minimum1.173464522
Maximum12.07756695
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size886.0 KiB
2022-06-10T17:27:40.890104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.173464522
5-th percentile3.539557229
Q14.44571828
median5.397394001
Q36.633313795
95-th percentile11.37145969
Maximum12.07756695
Range10.90410243
Interquartile range (IQR)2.187595515

Descriptive statistics

Standard deviation2.354330606
Coefficient of variation (CV)0.3898166143
Kurtosis0.381734206
Mean6.039585076
Median Absolute Deviation (MAD)1.111120574
Skewness1.143342389
Sum684810.433
Variance5.542872601
MonotonicityNot monotonic
2022-06-10T17:27:40.985102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.059701532
 
< 0.1%
6.4844061672
 
< 0.1%
4.0093196272
 
< 0.1%
6.5381441362
 
< 0.1%
5.4647573192
 
< 0.1%
4.8814229912
 
< 0.1%
5.5214126292
 
< 0.1%
6.1307470992
 
< 0.1%
3.7728204432
 
< 0.1%
4.0154132632
 
< 0.1%
Other values (113346)113367
> 99.9%
ValueCountFrequency (%)
1.1734645221
< 0.1%
1.3144926241
< 0.1%
1.4128138651
< 0.1%
1.534233721
< 0.1%
1.5391744791
< 0.1%
1.5845852891
< 0.1%
1.5865970781
< 0.1%
1.626930361
< 0.1%
1.6320922131
< 0.1%
1.6321518181
< 0.1%
ValueCountFrequency (%)
12.077566951
< 0.1%
12.029134851
< 0.1%
12.024259461
< 0.1%
12.021030171
< 0.1%
12.018917351
< 0.1%
12.015290491
< 0.1%
12.014420221
< 0.1%
12.008082041
< 0.1%
12.006449631
< 0.1%
12.001387021
< 0.1%

n11
Real number (ℝ≥0)

Distinct113370
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.699805939
Minimum1.500005729
Maximum1.899994972
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size886.0 KiB
2022-06-10T17:27:41.082107image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1.500005729
5-th percentile1.519940048
Q11.599951801
median1.699381553
Q31.800060988
95-th percentile1.87957317
Maximum1.899994972
Range0.399989243
Interquartile range (IQR)0.2001091875

Descriptive statistics

Standard deviation0.1154061583
Coefficient of variation (CV)0.0678937258
Kurtosis-1.201676636
Mean1.699805939
Median Absolute Deviation (MAD)0.100057787
Skewness0.001989261382
Sum192735.896
Variance0.01331858138
MonotonicityNot monotonic
2022-06-10T17:27:41.180106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.545576922
 
< 0.1%
1.720387172
 
< 0.1%
1.5491967182
 
< 0.1%
1.8933297622
 
< 0.1%
1.5262923872
 
< 0.1%
1.6994573822
 
< 0.1%
1.8811792822
 
< 0.1%
1.7962847652
 
< 0.1%
1.6921338542
 
< 0.1%
1.5931609862
 
< 0.1%
Other values (113360)113367
> 99.9%
ValueCountFrequency (%)
1.5000057291
< 0.1%
1.5000094761
< 0.1%
1.5000205181
< 0.1%
1.5000244541
< 0.1%
1.5000253081
< 0.1%
1.5000253731
< 0.1%
1.5000279481
< 0.1%
1.5000281821
< 0.1%
1.5000299471
< 0.1%
1.5000333841
< 0.1%
ValueCountFrequency (%)
1.8999949721
< 0.1%
1.8999877971
< 0.1%
1.8999848421
< 0.1%
1.8999842521
< 0.1%
1.8999811261
< 0.1%
1.899980881
< 0.1%
1.8999807181
< 0.1%
1.899971641
< 0.1%
1.8999637641
< 0.1%
1.8999588921
< 0.1%

n12
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
0
112216 
1
 
1171

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113387
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0112216
99.0%
11171
 
1.0%

Length

2022-06-10T17:27:41.271102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T17:27:41.338104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0112216
99.0%
11171
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0112216
99.0%
11171
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number113387
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0112216
99.0%
11171
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common113387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0112216
99.0%
11171
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII113387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0112216
99.0%
11171
 
1.0%

n13
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.3 MiB
0
103160 
1
 
10227

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters113387
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0103160
91.0%
110227
 
9.0%

Length

2022-06-10T17:27:41.394102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T17:27:41.463106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0103160
91.0%
110227
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0103160
91.0%
110227
 
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number113387
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0103160
91.0%
110227
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
Common113387
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0103160
91.0%
110227
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII113387
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0103160
91.0%
110227
 
9.0%

n14
Real number (ℝ≥0)

Distinct113383
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4991697331
Minimum3 × 10-6
Maximum0.999989791
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size886.0 KiB
2022-06-10T17:27:41.537104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3 × 10-6
5-th percentile0.04912658
Q10.2489890355
median0.498620263
Q30.74990486
95-th percentile0.9497758781
Maximum0.999989791
Range0.999986791
Interquartile range (IQR)0.5009158245

Descriptive statistics

Standard deviation0.2889957541
Coefficient of variation (CV)0.5789528791
Kurtosis-1.201052449
Mean0.4991697331
Median Absolute Deviation (MAD)0.250357071
Skewness0.004446066482
Sum56599.35853
Variance0.08351854591
MonotonicityNot monotonic
2022-06-10T17:27:41.636104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5974638432
 
< 0.1%
0.2397546262
 
< 0.1%
0.3511144472
 
< 0.1%
0.1935529152
 
< 0.1%
0.6312203431
 
< 0.1%
0.392718071
 
< 0.1%
0.1868132021
 
< 0.1%
0.4318764981
 
< 0.1%
0.6448731371
 
< 0.1%
0.686320811
 
< 0.1%
Other values (113373)113373
> 99.9%
ValueCountFrequency (%)
3 × 10-61
< 0.1%
3.16 × 10-51
< 0.1%
3.95 × 10-51
< 0.1%
4.57 × 10-51
< 0.1%
0.0001005031
< 0.1%
0.0001008321
< 0.1%
0.0001022291
< 0.1%
0.0001039821
< 0.1%
0.0001046481
< 0.1%
0.0001069721
< 0.1%
ValueCountFrequency (%)
0.9999897911
< 0.1%
0.9999890571
< 0.1%
0.9999796321
< 0.1%
0.9999600271
< 0.1%
0.9999599721
< 0.1%
0.9999563221
< 0.1%
0.9999487721
< 0.1%
0.9999451661
< 0.1%
0.9999369041
< 0.1%
0.9999301571
< 0.1%

n15
Real number (ℝ≥0)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.002769277
Minimum0
Maximum6
Zeros16263
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size886.0 KiB
2022-06-10T17:27:41.716102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.003302643
Coefficient of variation (CV)0.6671517049
Kurtosis-1.255187304
Mean3.002769277
Median Absolute Deviation (MAD)2
Skewness-0.003842175665
Sum340475
Variance4.013221477
MonotonicityNot monotonic
2022-06-10T17:27:41.777102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
516372
14.4%
016263
14.3%
116258
14.3%
616233
14.3%
316201
14.3%
416118
14.2%
215942
14.1%
ValueCountFrequency (%)
016263
14.3%
116258
14.3%
215942
14.1%
316201
14.3%
416118
14.2%
516372
14.4%
616233
14.3%
ValueCountFrequency (%)
616233
14.3%
516372
14.4%
416118
14.2%
316201
14.3%
215942
14.1%
116258
14.3%
016263
14.3%

label
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing85065
Missing (%)75.0%
Memory size4.9 MiB
0.0
23562 
1.0
4760 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters84966
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.023562
 
20.8%
1.04760
 
4.2%
(Missing)85065
75.0%

Length

2022-06-10T17:27:41.848102image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-06-10T17:27:41.917106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.023562
83.2%
1.04760
 
16.8%

Most occurring characters

ValueCountFrequency (%)
051884
61.1%
.28322
33.3%
14760
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number56644
66.7%
Other Punctuation28322
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
051884
91.6%
14760
 
8.4%
Other Punctuation
ValueCountFrequency (%)
.28322
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common84966
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
051884
61.1%
.28322
33.3%
14760
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII84966
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
051884
61.1%
.28322
33.3%
14760
 
5.6%

Interactions

2022-06-10T17:27:32.204153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:10.658925image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:12.234038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:13.991263image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:15.581394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:17.285588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:18.902609image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:20.503709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:22.295709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:23.946724image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:25.707937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:27.260155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:28.850554image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:30.644999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:32.311148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:10.778924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:12.347636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:14.100262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:15.692392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:17.398606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:19.014604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:20.616709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:22.417711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:24.064726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:25.817941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:27.374153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:28.968201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:30.752477image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:32.425152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:10.894040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:12.469262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:14.216777image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:15.808979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:17.519607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:19.131607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:20.737709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:22.536707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:24.191727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:25.934938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:27.499152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:29.090201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:30.867476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:32.533150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:11.006038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:12.585257image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:14.328394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:15.917979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:17.633605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:19.246122image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:20.852709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:22.650711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:24.308726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:26.044938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:27.611758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:29.206802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:30.977501image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:32.641153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:11.114040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:12.815262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:14.439393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:16.027976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:17.747607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:19.356707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:21.103709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:22.764710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:24.428727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:26.153530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:27.720756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:29.462804image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:31.089503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:32.751153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:11.226039image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:12.934259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:14.555393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:16.141979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:17.864606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:19.469711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:21.220709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:22.880711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:24.546728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:26.266532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:27.835871image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:29.580801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:31.203513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:32.859150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:11.337038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:13.051259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:14.667393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:16.251979image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:17.978604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:19.581708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:21.335706image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:22.994727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:24.657728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:26.375535image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:27.944938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:29.698815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:31.311375image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:32.970150image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:11.453038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:13.170259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:14.783394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:16.365975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:18.095607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:19.696709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:21.455709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:23.110725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:24.773727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:26.487152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:28.058935image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:29.819825image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:31.428379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:33.078151image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:11.562038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:13.285262image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:14.895393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:16.475978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:18.209605image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:19.809709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:21.570709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:23.220729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:24.884323image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:26.599154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:28.171946image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:29.933826image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:31.536377image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:33.188148image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:11.673036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:13.401261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:15.007394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:16.588588image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:18.326606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:19.924708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:21.687711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:23.331727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:24.999320image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:26.707153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:28.284559image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:30.052823image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:31.648572image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:33.434756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:11.782040image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:13.516259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:15.116394image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:16.697585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:18.436604image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:20.043709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:21.805709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:23.441729image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:25.108940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:26.813154image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:28.395560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:30.167836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:31.755587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:33.545758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:11.890038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:13.631259image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:15.229393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:16.942585image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:18.551606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:20.156709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:21.921712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:23.568725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:25.355940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:26.920155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:28.508561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:30.282836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:31.865608image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:33.661380image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:12.010038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:13.756261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:15.351393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:17.061584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:18.673606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:20.277708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:22.046711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:23.700727image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:25.474941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:27.038153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:28.629560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:30.406836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:31.986147image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:33.771381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:12.120038image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:13.871261image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:15.464393image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:17.171587image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:18.788606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:20.390709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:22.176709image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:23.821725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:25.591941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:27.146159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:28.739557image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:30.523450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-06-10T17:27:32.094152image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-06-10T17:27:41.990104image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-06-10T17:27:42.152114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-06-10T17:27:42.314114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-06-10T17:27:42.596116image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-06-10T17:27:42.752114image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-06-10T17:27:34.283399image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-06-10T17:27:35.204209image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-06-10T17:27:35.883210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-06-10T17:27:36.208665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexidgenders11s12s13s16s17s18s48s52s53s54s55s56s57s58s59s69s70s71n1n2n3n4n5n6n7n8n9n10n11n12n13n14n15label
00b'gAAAAABinOicS09vrmgh0_JyEHihI13ptO0rCyHP7l76be71PWA2ReUc4HUQn16Fya1z8_VStNnFGaXJF262CgsuMPzOaknSeg=='MYN1DDB01NaNkKNaNNaNBNaNxop: Db16.1446661.98944122.318385-32.8392770.017176-9.1260561.7322913.6985044.8045171.544484000.63122050.0
11b'gAAAAABinOiWGC1WhR6WYP0DA5ssGv9rIekrWUwCdJ8FvkVcSUl2AquMfWqtOqs3AQYGxS13wQv9Tx4GEkPEl5RnbchazqsZcw=='MYY1DDB11NaNNaNNaNNaNBNaNxop: Db7.1445580.84486636.197768-32.5765970.013857-9.0982871.5058856.7913576.1104161.712354000.39274631.0
22b'gAAAAABinOibTcOBFIVeA4nVF3FuFz_QX3ZlPPFc21gS9EYdw6Wo8Y5agbzfD6hhsaXZCBdrUQVPpZBXYsODc2PDjER2DX5QcA=='MYY1DDB01NaNNaNNaNNaNBNaNxop: Db6.9232361.04201867.824401-32.5105440.013943-9.2348941.5038284.1096853.9532261.804260000.22253720.0
33b'gAAAAABinOig-g3-Q1ggjlMhfUSdn21Aj5yVVeVvXbisuGUmadvbBh5W28jivd2vgGUWVfHtMdC6vNLrDyFM5NgzILAOorgWGA=='FYY1DDB01NaNNaNNaNNaNBNaNxop: Db5.7498400.78143928.256767-32.3986790.010387-9.3780251.4858637.2658764.5594191.537645000.15440940.0
44b'gAAAAABinOiXdoaNUzihOSbyY1tjWtd5EgMaXkkvH6SVbyppsCh4sW4X5QGqFrLNAcfMQ4NPHOLqbNUVKU-5xxvWCwb5tT91Pw=='MNY1BDD1lNaNNaNNaNNaNBNaN~1op: Dd14.7719591.24818832.300011-32.3967460.016289-9.2619621.6192103.7376474.0520031.637831010.73756010.0
55b'gAAAAABinOiWbgAxe8Uy9tboiJGZEYK7zcGy6fv8_5Ao4nwN9iCZx70at4UsfDvb3X4JL1Om9_sgAPBUiuuize3v7CwcsFm6Bw=='MYY1DCB11NaNNaNNaNNaNBNaNxop: Cb11.5333972.06274992.732090-32.8655950.008230-8.8859641.84586210.66065111.7041211.568647000.68764060.0
66b'gAAAAABinOiYFRgmHZu5sLaAYKtwJbRhPnjCYPVxQPNfCegG_-eyGDg_9F43uYQqD9Ok9MKZlmuyQmf9LY0pk0GOZFDqZbFMIA=='MYY1BDB1lNaNNaNNaNNaNBNaN~1op: Db16.8045802.67138697.378877-32.5771930.007366-8.8180201.5353698.15503111.5554411.543350000.98384051.0
77b'gAAAAABinOiXQjg5rjPZsHSAQrJhP8u_21fwETW89EFNxR9FtB8Fh3PlYL5EV8PSGKzRZ0gyBPCkfS3ldV44cbP12fTatgRTFw=='MYY1BCB0lNaNNaNNaNNaNANaN~1op: Cb12.4963992.43798137.066580-32.3120620.022486-9.2493171.8111642.8228416.5905231.694829000.77779800.0
88b'gAAAAABinOic9rpK09lv7Q2k7bMbzXsT0ZluA8SfT7x0Zu92oMUyQExuOhSYx-UBx1JLSBOOyN9PptyKg6nAs-HIBfVBNV3wOw=='FYY1DDB01NaNNaNNaNNaNBNaNxop: Db17.6231422.28403568.069883-32.3590320.013593-9.0423501.51000210.66168611.2769861.779480000.55701900.0
99b'gAAAAABinOiWr-5MKlXYJ9hkIMYLh2XNHJeYWIIpt94IyPKqeNbqH9nEZdgg27APXCQ4Nzhzsc8-SqrrXSgGXEMGE3oSLzNZFw=='MYN1DDB01NaNkkNaNNaNBNaNxop: Db4.3879130.83772768.326920-32.6410600.012796-9.3260151.3839082.6718423.9403051.716165000.21287410.0

Last rows

df_indexidgenders11s12s13s16s17s18s48s52s53s54s55s56s57s58s59s69s70s71n1n2n3n4n5n6n7n8n9n10n11n12n13n14n15label
11337785055b'gAAAAABinOi6zJgX0FYqKLpaYQy0n4T7ramdtG9HFavStHlnZD3lbUJaqa1JlwRhAVNmDymzAawhcQO8HIhCqFiQtiNtL-YQsQ=='MYY1DDB0lNaN2kNaNNaNBNaNxop: Db9.0907481.29691332.419601-32.8820830.005606-9.0475331.7210725.5685897.0302841.715815000.4177930NaN
11337885056b'gAAAAABinOjYpI4tYDhEaE6_TnZmrCXhbw1xx-kIo0nBGVVet_njWWKCpUAqDlGY_IiLKFUUhf_9odKpS-KBeKggxm0-LinpIA=='MNY1BDB01NaNNaNNaNNaNBNaN~1op: Db9.0629761.52233237.935921-32.8843120.010559-9.0989311.7504565.3832446.3894011.799633000.2197364NaN
11337985057b'gAAAAABinOis8yfNwXL7Kt6iRUd_A5Vd2eihkJqs5xbL962BXfvB-RuIETTd5MWquFeJfXiI07SoYp5EjVdtsJr2NIu16JJCeg=='MYY1BDB1lNaNNaNNaNNaNBNaN~1op: Db13.8315260.87110186.081569-32.7737120.004776-8.7380721.5253483.41815510.1533211.568147100.2734466NaN
11338085058b'gAAAAABinOitJ9_osMoCGx40ogPuQRjX7zn2EYbITg_b8AeXSFAXNU1MA-VIZizTfe9pRVisKKAU9rXB30hsS6G4qo1_U33QTA=='MYN1DDB11NaNNaNNaNNaNBNaNxop: Db16.3524032.32246092.104030-32.7737270.004826-8.8174661.9212669.11610411.1178001.518039000.9843770NaN
11338185059b'gAAAAABinOi48oACwvgwIxm-EKRlI8J-lwNL_4EQhwIkhbeK9j8VAgHR-IWzOlY7yVxiydHJP9Iyx2ZS3UOMaFy8GTe1t4dnAA=='MYY0BDB1lNaNNaNNaNNaNBNaN~1op: Db4.6897880.88881263.508845-32.5764290.021379-9.2903721.4640673.7888343.5732331.656310010.5594752NaN
11338285060b'gAAAAABinOjbnJVk2-nOVQsYB9p4DK26fTLLik_UR2H0ZdXBBqQbEPbsImv7TCwlFJpVNo_UoUMU0UhCmpyfcqDLLMAZl-gSTQ=='MYY1CCC1lNaNNaNNaNNaNBNaNC`op: Cc10.5473811.45266078.000085-32.5782210.011017-8.9943041.5818169.2656575.6048451.746737000.7511000NaN
11338385061b'gAAAAABinOi7ixyXrlKYlx8D9i0-TIPD5elP2k-vuekn1hlXH57UuJovPEBaRy3YoKsLS-BCkQALy4lEyRCREjIPvNkqsXSIiw=='MYY1BCC1lNaNNaNNaNNaNBNaN~1op: Cc10.1491172.16823966.011115-32.4472820.019550-9.1312751.5261107.8231665.4583731.669676000.9032943NaN
11338485062b'gAAAAABinOi31zWSlD0OMhbBd3_weh7Kq6aPeO4yYqnsexwC8nUb0gNYBt8Ulk3l9xJPa-Ej-248UZlSewhrn9HkKagsQEspxQ=='MNY1DAC11NaN2kNaNNaNBNaNxop: Ac3.7443800.66132822.112016-32.3511650.015461-9.2495291.5055476.4389853.4299281.500925000.5718953NaN
11338585063b'gAAAAABinOjIe7jFVk9k7jiH8Y3rdpUHDTZG2T2isunpNUJuG6H5KXO9l9AVdMsdB6jYN-XX-FkuLZxfrUfHA2NGGq6rrEU4cw=='MNY1BDD1lNaNNaNNaNNaNBNaN~1op: Dd10.1946151.55786322.433086-32.3638710.010612-9.2291581.8332865.3833116.2125521.730987000.6556936NaN
11338685064b'gAAAAABinOiiLo4KNZVgClHgtOFRzEU9O97My6MowJFa4ybRyBq3uDzTJVKrJfS7DzU2nqAT06tveVEl23nycx348dbcJqdryA=='MYY1DDB01NaNNaNNaNNaNBNaNxop: Db10.1096541.51792306.809017-32.6445580.029553-9.2256711.8373383.3244904.8906861.844714000.7852512NaN